Unless otherwise indicated herein, the approaches described in this section are not prior art to the claims in this application and are not admitted to be prior art by inclusion in this section.
Embodiments relate to time-series data, and in particular, to a multiple-representation approach for storing time series data. Time series are sequences of recorded data points ordered along the time dimension, that often arise when monitoring industrial processes, business characteristics and Key Performance Indicator (KPI) developments, financial parameters, scientific experiments, physical measurements, and others.
Time series analysis are data analysis techniques used as a basis for business planning and decision support in many application domains. There thus exists an ever-present interest in research and industry, to query, mine, and analyze time series. A task of time series analysis is to identify patterns, sequences, correlations, and characteristics within and between time series data, and to draw conclusions based on those observations.
Recent developments in computer hardware and sensor technology have resulted in increasing availability of continuous measurements recorded with sampling rates of up to MHz or higher. This leads to fine grained time series, and hence to a substantially increasing data volume and data streaming frequencies.
In addition, the amount of available sensors is growing substantially, especially given new application scenarios such as the Internet of Things (IoT). As an example, location based services on mobile phones offer potentially millions of sensors providing data at the same time.